System for Cardiac Pathology Detection and Characterization

ABSTRACT

A system for heart performance characterization and abnormality detection includes an interface for receiving digitized electrical signals representing blood pressure waveforms over one or more heart beat cycles. The digitized electrical signals comprise, a first digital data sequence representing normal blood pressure of a patient, a second digital data sequence representing random blood pressure of a normal patient and a third digital data sequence representing a potentially abnormal blood pressure of a patient. A complexity processor calculates first, second and third complexity indices for the corresponding first, second and third digital data sequences respectively. A correlation processor uses the calculated first, second and third complexity indices to calculate one or more measures indicating deviation of the potentially abnormal blood pressure of the patient from a normal value.

This is a non-provisional application of provisional application Ser.No. 61/184,352 filed Jun. 5, 2009, by H. Zhang.

FIELD OF THE INVENTION

This invention concerns a system for heart performance characterizationand abnormality detection involving calculating measures indicatingdeviation of potentially abnormal blood pressure of a patient from anormal value.

BACKGROUND OF THE INVENTION

Invasive and non-invasive blood pressure analysis is used for heart andcardiac circulation monitoring and function evaluation. Usually bloodpressures measurement at different sites (such as left atrial, rightatrial) and cardiac output calculation are utilized to diagnose andcharacterize cardiac function and patient health status. Known clinicalmethods for blood pressure analysis do not fully capture patient healthinformation from monitored pressure. Some known systems use a bloodvelocity waveform and calculate magnitude-squared coherence (MSC) foranalysis of cardiovascular diseases (such as material hypertensionduring pregnancy) but fail to provide qualitative and quantitativeanalysis and evaluation of a blood pressure waveform.

The cardiovascular system comprises a pump (the heart), a carrier fluid(blood), a distribution system (arteries), an exchange system (capillarynetwork), and a collecting system (venous system). Blood pressure is thedriving force that propels blood along the distribution network.Hemodynamic signal (especially blood pressure waveform and data)analysis is used for characterization of cardiac pathology anddisorders. Accurate hemodynamic signal (invasive and non-invasive bloodpressure signal) measurement and (True positive and false negative rate)evaluation are desirable to monitor patient health status. Known methodsof using blood pressure signals typically focus on stroke volume andcardiac output calculation. Further known hemodynamic signal andpressure waveform analysis is mostly used for qualitative diagnosis andanalysis of the cardiac functions, such as low perfusion and pressureamplitude variation. Some known methods use pressure signal integrationanalysis and calculation (e.g. based on ventricular end-diastolic volume(EDV) and end-systolic volume (ESV)) for cardiac function evaluation,such as SV (stroke volume) and cardiac output. But these methods fail tocomprehensively and effectively use the hemodynamic signals and waveformcharacteristics.

Usually changes and distortions due to cardiac malfunctions andarrhythmia affect the cardiac tissue earlier than electrophysiologicalcharacteristics. Known clinical systems fail to comprehensively detectearly changes and deviations of such malfunctions (which are usuallysmall), such as blood pressure magnitude and ECG signal distortions.Electrophysiological signals (ECG, ICEG) and hemodynamic signals capturedifferent information concerning cardiac diseases and heartmalfunctions. Known systems use different methods to interpretelectrophysiological signals, such as RR wave detection, ST elevation,PQRST morphology deviation. However, electrophysiological signals areoften corrupted by electrical noise and bio-artifacts, such as powerline noise and patient movement. Hemodynamic signals (such as bloodpressure) may provide better noise immunity and cardiac functionanalysis stability. A system according to invention principles addressesthe identified deficiencies and related problems and providesqualitative and quantitative analysis of a blood pressure waveform.

SUMMARY OF THE INVENTION

A system improves sensitivity and reliability of blood pressureinterpretation by analyzing and characterizing blood pressure waveformdata using hemodynamic waveform signal coherence, for example, toidentify and characterize cardiac disorders. A system for heartperformance characterization and abnormality detection includes aninterface for receiving digitized electrical signals representing bloodpressure waveforms over one or more heart beat cycles. The digitizedelectrical signals comprise, a first digital data sequence representingnormal blood pressure of a patient, a second digital data sequencerepresenting random blood pressure of a normal patient and a thirddigital data sequence representing a potentially abnormal blood pressureof a patient. A complexity processor calculates first, second and thirdcomplexity indices for the corresponding first, second and third digitaldata sequences respectively. A correlation processor uses the calculatedfirst, second and third complexity indices to calculate one or moremeasures indicating deviation of the potentially abnormal blood pressureof the patient from a normal value.

BRIEF DESCRIPTION OF THE DRAWING

FIG. 1 shows a system for heart performance characterization andabnormality detection, according to invention principles.

FIG. 2 illustrates complexity calculation for a hemodynamic pressuresignal based on adjustable symbolization, according to inventionprinciples.

FIG. 3 shows a model and associated calculation for complexity coherencedetermination of a blood pressure signal, according to inventionprinciples.

FIG. 4 shows a flowchart of a process for blood pressure waveform signalbased coherence detection and interpretation, according to inventionprinciples.

FIG. 5 shows hemodynamic pressure monitoring and interpretation based oncoherence calculation, according to invention principles.

FIG. 6 shows multiple channel pressure signal coherence analysis andmonitoring for cardiac event and pathology diagnosis, according toinvention principles.

FIG. 7 shows a flowchart of a process for complexity calculation ofsymbolic strings S=s₁s₂ . . . s.

FIG. 8 shows a flowchart of a process used by a system for heartperformance characterization and abnormality detection, according toinvention principles.

DETAILED DESCRIPTION OF THE INVENTION

A system improves sensitivity and reliability of blood pressure signalinterpretation by analyzing and characterizing blood pressure waveformdata using hemodynamic waveform signal coherence, for example. Thesystem identifies nonlinear signal and waveform changes due topathologies or medical events by accurate interpretation of an invasiveand non-invasive blood pressure waveform signal and data. The systemidentifies cardiac disorders, categorizes cardiac arrhythmia,characterizes pathological severity, predicts life-threatening events,and evaluates drug delivery effects.

The system provides patient health information from pressure waveformmonitoring, including waveform morphology variation, identified waveformcomponents, amplitude and frequency variability, blood pressure patternsand blood pressure nonlinearity correlation with cardiac arrhythmias.The system combines hemodynamic signal (blood pressure and waveformmorphology) analysis and electrophysiological signal analysis to improveclinical signal interpretation for use by healthcare workers. The systemanalyzes a blood pressure waveform using windowed coherence, complexitycoherence, multi-bandwidth and multi-channel coherence for cardiacstatus characterization, for example. The system improves sensitivityand reliability of analysis and interpretation of patient cardiac statusand health and particularly nonlinear changes in hemodynamic signals dueto the arrhythmia or cardiac events and provides predictive indicationof different types of arrhythmias and cardiac malfunction location, suchas AF (atrial fibrillation) and MI (myocardial ischemia).

In one embodiment, different kinds of signals, includingelectrophysiological signals, oximetric signals (such as SPO2), are usedin combination for precise synchronization of hemodynamic signals toimprove sensitivity and accuracy of signal analysis. Multi-channelsignal pattern and mode analysis is performed (including single-channelcoherence, cross-channel coherence) of hemodynamic signals. The systemin a further embodiment uses an artificial neural network (ANN) foranalysis but in other embodiments may use a fuzzy system or expertsystem, for example. The system maps hemodynamic signal data of a heartand circulation system (including tissue, function, activities) tomedical conditions based on multi-channel blood pressure mode andpattern analysis using external and internal pressures derived fromdifferent locations in patient anatomy. The system may be implemented insoftware or firmware or a combination of both in medical devices in ICD(implantable cardioverter-defibrillator) equipment using blood pressurepattern analysis.

Hemodynamic signals, such as invasive and non-invasive blood pressure(IBP and NIBP) waveforms and related waveform calculations (such asdP/dt), are used to interpret, evaluate and quantitatively characterizeheart function, arrhythmias, and patient health status. The systemadvantageously employs a wider range of properties and characteristicsof hemodynamic signals and waveforms for cardiac function and rhythmdiagnosis, including waveform morphology variation and spectrumcoherence. The system advantageously determines coherence parameters ofa hemodynamic signal and pressure waveform at multiple cardiac sites andmaps the parameters to heart conditions using predetermined mappinginformation.

FIG. 1 shows system 10 for heart performance characterization andabnormality detection. Magnitude based pressure waveform analysis maynot be able to accurately characterize nonlinear changes in a bloodpressure representative signal waveform (indicating morphology) orfrequency component distortion of blood pressure signals. System 10 usescoherence analysis (which is based on complexity calculation and onspectrum distribution) for blood pressure pattern analysis and waveformfeature quantification. The system determines coherence by extracting alinear association by comparing a baseline blood pressure signal and anon-going blood pressure waveform. Due to cardiac events, such asarrhythmias, this linear association varies because of signal componentchanges, such as frequency component changes and such changes areindicated by complexity calculations. System 10 uses cardiac hemodynamicsignal analyses of invasive (intra-cardiac) blood pressure andnon-invasive blood pressure to qualitatively and quantitatively test andevaluate heart activity and abnormality, such as tissue and cardiacfunctions. A system 10 complexity calculation characterizes anddifferentiates cardiac pathologies and differentiates between severityof cardiac signals having the same rhythm. A detailed illustration ofcalculation of complexity is shown in the appendix.

System 10 comprises at least one computer system, workstation, server orother processing device 30 including interface 12, repository 17,complexity processor 19, correlation processor 15, data processor 20,calculation processor 24 and a user interface 26. Interface 12 receivesdigitized electrical signals representing blood pressure waveforms overone or more heart beat cycles. The digitized electrical signalscomprise, a first digital data sequence representing normal bloodpressure of a patient, a second digital data sequence representingrandom blood pressure of a normal patient and a third digital datasequence representing a potentially abnormal blood pressure of patient11. Complexity processor 19 calculates first, second and thirdcomplexity indices for the corresponding first, second and third digitaldata sequences respectively. Correlation processor 15 uses thecalculated first, second and third complexity indices to calculate oneor more measures indicating deviation of the potentially abnormal bloodpressure of the patient from a normal value. Calculation processor 24determines measures derived from Fourier transform values of componentsof a received digitized electrical signal.

FIG. 2 illustrates complexity calculation performed by complexityprocessor 19 (FIG. 1) using hemodynamic pressure signal based adjustablesymbolization. Different methods may be used for symbolic complexityindex calculation and computation. In one embodiment, complexityprocessor 19 adaptively selects between different types of symboliccomplexity index calculation in response to data indicating clinicalapplication and accuracy or resolution requirements, to capture smallersignal changes in a signal, for example. A user is also able to select aparticular hemodynamic signal calculation based on a desired clinicalapplication. A first type of symbolic complexity index calculationcomprises single level symbolization calculation 203 employing adaptablethreshold 205 for symbolization of a blood pressure signal into binary(0 and 1) strings having a complexity value C of 3. A second type ofsymbolic complexity index calculation comprises multi-levelsymbolization calculation 207 employing multiple adaptable thresholdsfor symbolization of a blood pressure signal into binary (0 and 1)strings having four components and a complexity value C of 6.

FIG. 3 shows a model and associated calculation for complexity coherencedetermination of a blood pressure signal performed by complexityprocessor 19. Specifically, system 10 in step 322 processes waveformdata representing normal heart contraction and initialization 318 aswell as waveform data representing abnormal factors such as arrhythmias320 and additional noise 326, to provide a blood pressure data samplesequence. In one embodiment, complexity coherence is determined bycalculating and comparing a complexity value for different parts of ahemodynamic signal, specifically a baseline episode and a currentongoing real time episode for the blood pressure data sample sequence.Processor 19 calculates a complexity coherence value 315 using acomplexity value determined for a data sample series Si for normal bloodpressure waveform data 303, a complexity value determined for a datasample series Sr for a random blood pressure waveform data 305 and acomplexity value determined for a data sample series Sj for potentiallyabnormal blood pressure waveform data 307. Processor 19 calculatescomplexity coherence value 315 to capture nonlinear changes of a currentongoing pressure waveform. In complexity coherence calculation 315, theblood pressure digital data stream time series Sr Si Sj are synchronizedand use a time window having a starting edge comprising a peak (Maximum)of one of the waveforms or use a time stamp (such as a stamp indicatingEoS (End Systolic) point, EoD (End of Diastolic) point or a Minimumvalue, for example.

System 10 applies complexity theory to identify waveform data having thehighest complexity. In normal operation, a normal heart contracts andsqueezes and operates in an orderly manner and linearly with normalheart control rate. In response to an abnormal event, such as a cardiacpathology event or drug delivery, the normal working harmonics of thehemodynamic sequence may change and vary. Hemodynamic waveform signaldata may be characterized as having a linear portion and a nonlinearportion. The nonlinear portion in a cardiac signal may increase due toseverity of the cardiac function abnormality. When cardiac rhythm fallsinto arrhythmia (e.g. because of a clinical event), the nonlinear partincreases which is identified by the system as a potential abnormalcardiac change. The nonlinearity changes in a blood pressure waveformare advantageously characterized by complexity coherence as:

$\begin{matrix}{{Complexity\_ coherence} = {\frac{{{C\left( s_{j} \right)} - {C\left( s_{R} \right)}}}{{{C\left( s_{i} \right)} - {C\left( s_{R} \right)}}}.}} & {\text{-}{equation}\mspace{14mu} 1}\end{matrix}$

In which, S_(i), S_(j) and S_(R) are complexity calculation indexes fordifferent time series, normal (baseline signals), ongoing real timesignals, and random signals. S_(R) typically does not change for aparticular portion and length of a blood pressure waveform time dataseries, but is adjustable by change of a complexity coherencecalculation window portion of the pressure waveform. The coherencecalculation time window may be changed from one heart beat to multipleheart beats, for example, and a beat is identified using an ECG signal.

A beat for a blood pressure signal is indicated by Peak to Peakpressure, EoS (End-of-Systolic) to EoS, Minimum to minimum pressure, forexample. C(s_(j))−C(s_(R)) represents the complexity distance(nonlinearity) from a current blood pressure waveform to a randomwaveform (e.g., random waveform data derived by a computer).C(s_(i))−C(s_(R)) represents a complexity distance from a normal(baseline) pressure waveform of the patient concerned to the randomwaveform. The nonlinearity calculation identifies a difference between aperiodic blood pressure rhythm and random pressure waveform. Randomsignals are used as a maximum complexity to measure changes anddistortions of blood pressure waveforms. Coherence (correlation) betweendifferent cardiac signals is determined by processor 19 (FIG. 1) by avariety of different equations in different formats. System 10 uses thecoherence (or correlation) to calculate and quantify nonlinear changes,mode and pattern alternations and distortions in hemodynamic signals dueto pathologies or clinical events.

Complexity coherence captures changes in hemodynamic signals in the timedomain, such as a fast rhythm or low perfusion. However, substantialinformation is also available in the frequency domain in datarepresenting frequency component changes in different bandwidths, forexample. System 10 uses multi-bandwidth spectrum coherence forinterpretation of hemodynamic pressure waveforms and signals. Individualhemodynamic waveforms are reconstructed using sinusoidal waveforms whichdivide a pressure waveform signal into a linear correlated portion and anonlinear (uncorrelated) portion. System 10 determines spectrumcoherence changes occurring due to clinical events and cardiac functionpathologies by analyzing the changes in particular blood pressurewaveform portions.

A system 10 mathematical model of spectrum coherence, in one embodiment,processes blood pressure waveform data with P constituent sine waves (Pis determined and adjusted by the system or user) that are taken at agiven point of a signal x for example, including a random phase sinusoidcomprising white noise so,

${x(t)} = {{\sum\limits_{i = 1}^{P}\left( {{A_{i}{\cos \left( {{\omega_{i}t} + \theta_{i}} \right)}} + {B_{i}{\cos \left( {{\omega_{i}t} + \phi_{i}} \right)}}} \right)} + {w(t)}}$

where w(t) is the white noise, A_(i) are amplitude terms and controlgain of correlated sinusoids and B_(i) are amplitude terms and controlgain of uncorrelated sinusoids and θ_(i), and φ_(i), are random phases.This applies to each sinusoid, i, such that 1≦i≦P where P is the numberof sinusoids. (In general, the number of correlated and uncorrelatedsinusoids need not be equal.) At this point the spectrum coherence modelat a given sinusoidal frequency is:

${\gamma \left( \omega_{i} \right)} = \frac{A_{i}^{2}}{A_{i}^{2} + B_{i}^{2}}$

A coherence function comprises a ratio of correlated to total(uncorrelated+correlated) power. The correlated power amplitudes can bewritten as:

${\gamma_{xx}(f)} = \frac{\frac{1}{N}{{\sum\limits_{n = 1}^{N}{X_{n}(f)}}}^{2}}{\frac{1}{N}{\sum\limits_{n = 1}^{N}{{X_{n}(f)}}^{2}}}$

where X_(n)(f) is the Fourier transform of the time series x(t).

Complexity coherence is calculated by combining the complexitycomputations of three series: baseline pressure signal series, on-goingpressure signal series, and random data series. A baseline pressuresignal is a normal signal from the patient (for example, acquired at thebeginning of a case, a previous recording of data or comparable pressuredata). Random data series are created using a computer which is used formaximum complexity comparison (Random signals have the biggestcomplexity calculation value and this increases with the length of thedata series). For example, a 100 data sample (calculation window) isutilized for complexity coherence calculation. The complexity value ofnormal baseline pressure signals is 15 (typically 10-20) while for arandom signal data series is 100. If the complexity of the real timeon-going pressure data is 16 and is comparable with the baseline signal,it means the real time signal is normal and complexity coherence is 0.99(near One) While, if the complexity calculation of the pressure signalis higher, for example 65, such as in arrhythmia or clinical eventcases, the complexity coherence is 0.41. The smaller the value ofcomplexity coherence, the more severe is arrhythmia.

System 10 calculates spectrum coherence in a similar manner tocomplexity coherence. For example, an on going real time data window is100 points, x₁, . . . , x_(N). The spectrum is calculated for this 100point (X₁, . . . , X_(N)) data series using a Fourier transform and thefollowing is calculated,

$\begin{matrix}{{\gamma_{xx}(f)} = \frac{\frac{1}{N}{{\sum\limits_{n = 1}^{N}{X_{n}(f)}}}^{2}}{\frac{1}{N}{\sum\limits_{n = 1}^{N}{{X_{n}(f)}}^{2}}}} & {\text{-}{equation}\mspace{14mu} 2}\end{matrix}$

In one embodiment system 10 uses equation 2 to derive a correlationpower amplitude parameter value. The normal range of spectrum coherencevalues is from 0 to 1 and for a continuous real time normal and healthyblood pressure waveform, the spectrum coherence value is 1 and in thecase of arrhythmia or clinical events such as myocardial ischemia or AF,the value is less than 1, for example.

System 10 derives a single measure comprising a spectrum coherence indexindicating signal change using data representing a substantial portionof an entire signal spectrum. The spectrum coherence index measureindicates changes in spectra of a signal and indicates how distinct thechanges are from a baseline template signal in various frequency bands.The frequency bandwidth templates signals are controlled and adjusted inresponse to clinical application and some frequency bands are not usedto avoid noise effects.

$\begin{matrix}{{{spectrum\_ coherence} = \frac{\overset{\_}{\gamma}}{\overset{\_}{\gamma} + {\sum\limits_{\Omega}{\Theta \left( {{{\hat{\gamma}\left( ^{j\; w} \right)} - \overset{\_}{y}}} \right)}}}}{with}{\Theta (x)} = {\begin{Bmatrix}{0.0,} & {if} & {x < {\Theta \overset{\_}{\gamma}}} \\{x,} & {if} & {x \geq {\Theta \overset{\_}{\gamma}}}\end{Bmatrix}.}} & {\text{-}{equation}\mspace{14mu} 3}\end{matrix}$

where 0<Θ<1. Thus, the Spectrum coherence index is near 1 when theongoing blood pressure waveform signal is healthy and spectrum coherenceindex of the blood pressure waveform decreases due to clinical eventsand pathologies.

The coherence analysis in one embodiment is used for analyzing a singlechannel blood pressure signal using a baseline beat waveform (or benign)signal. The coherence analysis in another embodiment is used foranalyzing multi-channel signals provided by using different bloodpressures obtained at different cardiac sites, for example, forcomparison and correlation to identify relative changes and variationbetween heart chambers and input and output characteristics. System 10processes multi-channel channel hemodynamic signals (invasive andnon-invasive blood pressure signals) to derive a function mappingindicating a heart or whole circulation system function. In the spectrumcoherence calculation a blood pressure digital data stream time seriesis synchronized for a spectrum coherence calculation. Thesynchronization uses a time window having a starting edge comprising apeak (Maximum) of one of the waveforms or uses a time stamp (such as astamp indicating EoS (End Systolic) point, EoD (End of Diastolic) pointor a Minimum value, for example.

FIG. 4 shows a flowchart of a process for blood pressure waveform signalbased coherence detection and interpretation performed by system 10(FIG. 1). System 10 determines signal coherence for different channelblood pressure signals and identifies location, timing, severity andtype of cardiac pathologies and diseases. System 10 quantifies a patternof nonlinear changes occurring due to cardiac function variation andpathologies. Interface 12 in step 403 acquires blood pressure signalsfrom multiple sensor channels and anatomical sites including differentcardiac chambers. Interface 12 in step 406 digitizes and filters theacquired blood pressure signals using a filter adaptively selected inresponse to data indicating clinical application (e.g. ischemiadetection, rhythm analysis application). In step 409 complexityprocessor 19 identifies different segments of blood pressure signal data(including peak, EoS, EoD sections, for example) of the filtereddigitized blood pressure signals and data processor 20 initializescalculation parameters such as time window size, time step,symbolization level and warning threshold value.

In step 412, complexity processor 19 calculates a complexity coherencevalue, spectrum coherence value and spectrum coherence index value forthe filtered, digitized blood pressure signals. Complexity processor 19calculates first, second and third complexity indices for thecorresponding first, second and third digital data sequencesrespectively. Correlation processor 15 uses the calculated first, secondand third complexity indices to calculate one or more measuresindicating deviation of the filtered, digitized blood pressure signals.In step 415 data processor 20 monitors the first, second and thirdcomplexity indices or values derived from these indices. Processor 20determines abnormality and relatively high nonlinearity and generates analert message in response to indices and values derived from theseindices or a variation in the indices and values derived from theseindices exceeding predetermined thresholds. Data processor 20 usespredetermined mapping information in repository 17, associating rangesof the indices or values derived from these indices with correspondingmedical conditions, in comparing the indices or values derived fromthese indices with the ranges and generates an alert message indicatinga potential medical condition.

If data processor 20 in step 415 identifies an abnormality or relativelyhigh nonlinearity and associated medical condition indicating cardiacimpairment or another abnormality, processor 20 in step 423 identifiesthe location, timing, severity and type of cardiac pathology andassociated disease based on the anatomical location at which the bloodpressure waveform is acquired. Processor 20 in step 423 also generatesan alert message identifying the medical condition and abnormality andcommunicates the message to a user in step 426 and stores or prints themessage and records the identified condition in step 429. If dataprocessor 20 in step 417 fails to identify an abnormality or relativelyhigh nonlinearity, in step 419 data processor 20 adaptively alters oneor more calculation parameters such as time window size, time step,symbolization level and warning threshold value to improve abnormalitydetection.

FIG. 5 shows hemodynamic pressure monitoring and interpretation based oncoherence calculation. System 10 calculates and plots complexitycoherence 505 and complexity coherence deviation 507 for blood pressurewaveform 503. Data processor 20 monitors calculated data 505 and 507 andidentifies normal points 509 and 511 as well as potentially abnormalpoints 513 and 515. The complexity coherence values identify events 513and 515 and associated times which may potentially provide pathologydetection and medical condition identification. Processor 20 furtherperforms statistical analysis, including deviation, standard deviationand a hypothesis test on the calculated data 505 and 507 to improvedetection of medical conditions.

System 10 (FIG. 1) analyzes blood pressure signal waveforms to identifyand characterize small changes in heart tissue and cardiac functions.For example, due to certain diseases, one heart chamber (such as a leftventricle) may not work at normal squeezing speed, which may change adiastolic time period (including time length, pressure amplitude, periodfrom EoS time to Maximum pressure time and rate of pressure changedP/dt). System 10 qualitatively and quantitatively captures andevaluates blood pressure waveform changes and identifies morphologypatterns for different heart operation modes. Thereby system 10 providesearly detection of negative changes in heart condition and functions andpredicts future patient (cardiac function and circulation system) statusand drug delivery indications. System 10 also employs combinations ofdifferent kinds of methods for blood pressure analysis including,cardiac stroke volume analysis, complexity calculation, time andamplitude ratio determination, complexity coherence calculation,spectrum and coherence calculation to improve hemodynamic pressurepattern interpretation. The pressure waveform analysis involves analysisof individual heart cycles and also averaging of data derived in asynchronized manner over multiple heart cycles (for example dataaveraging for the same heart cycle portion in different cycles) whichimproves signal to noise ratio.

FIG. 6 shows multiple channel pressure signal coherence analysis andmonitoring for cardiac event and pathology diagnosis. FIG. 6 shows anexample of IBP (invasive blood pressure) catheters 605 and 607 used forhemodynamic pressure monitoring and analysis for atrium and ventriclechambers in a heart. The catheters include multiple blood pressuretransducers and sensors 609 providing multiple pressure waveforms thatare used for complexity coherence calculation by processor 19. Dataprocessor 20 also performs coherence value pattern analysis usingcomplexity coherence values calculated for pressure waveform dataacquired at multiple different cardiac sites by catheters 605 and 607.Data processor 20 similarly determines and records data indicatingdifferent tissue working modes, such as of the left atrium, rightatrium, and ventricles for comparison with data derived from the patienton a previous occasion or with data derived from a population ofpatients sharing demographic characteristics with the patient (e.g.,age, weight, height, gender, pregnancy status). Data processor 20 usespredetermined mapping information in repository 17, associating rangesof complexity coherence values or values derived from complexity valueswith corresponding medical conditions, in comparing calculatedcomplexity related values for the multiple cardiac pressure waveformsacquired by catheters 605 and 607 with the ranges and generates an alertmessage indicating a potential medical condition. Data processor 20performs this complexity related data mapping for heart (internal) andwhole circulation system (external) analysis.

FIG. 8 shows a flowchart of a process used by system 10 for heartperformance characterization and abnormality detection. In step 812following the start at step 811, interface 12 receives digitizedelectrical signals indicating first, second and third digital datasequences representing blood pressure waveforms over one or more heartbeat cycles. Interface 12 substantially synchronizes the first, secondand third digital data sequences in response to a peak or minimum valuein the corresponding blood pressure waveforms. Interface 12 alsosubstantially synchronizes a digitized electrical signal in response toa peak or minimum value in the corresponding blood pressure waveforms.The received signals comprise a first digital data sequence representingnormal heart blood pressure of a patient, a second digital data sequencerepresenting random heart blood pressure of a normal patient and a thirddigital data sequence representing a potentially abnormal heart bloodpressure of a patient. In one embodiment, the first, second and thirdcomplexity indices comprise S_(i), S_(R) and S_(j) respectively and thecorrelation processor calculates at least one of, (a) C(s_(j))−C(s_(R))representing the complexity distance (nonlinearity) from a current bloodpressure waveform to a random waveform and (b) C(s_(i))−C(s_(R))representing a complexity distance from a normal (baseline) pressurewaveform to a random waveform. Correlation processor 15 calculates ratioof (a) and (b). The first digital data sequence representing normalblood pressure of a patient is provided from stored blood pressure dataof at least one of (i) the patient and (ii) a patient having similardemographic characteristics as the patient including at least one of ageweight, gender and height and similar medical conditions as the patient.

In step 815, data processor 20 performs a Fourier transform on adigitized electrical signal to derive data representing individualfrequency components of the digitized electrical signal. Calculationprocessor 24 in step 817 determines measures comprising first and secondvalues. The first value represents a summation of the square ofindividual Fourier transform values of corresponding individualcomponents of the individual frequency components. The second valuerepresents a square of the summation of individual Fourier transformvalues of corresponding individual components of the individualfrequency components. In step 819 correlation processor 15 uses thecalculated first and second values to calculate one or more measuresindicating deviation of the potentially abnormal blood pressure of thepatient from a normal value.

In step 820 complexity processor 19 calculates first, second and thirdcomplexity indices for the corresponding first, second and third digitaldata sequences respectively. Correlation processor 15 in step 822 usesthe calculated first, second and third complexity indices to calculateone or more measures indicating deviation of the potentially abnormalheart blood pressure of the patient from a normal range. Correlationprocessor 15 also calculates ratios (e.g., using equations 2 and 3)employing the first and second values in deriving a measure indicatingdeviation of the potentially abnormal blood pressure of the patient froma normal value.

Data processor 20 in step 827 monitors the first, second and thirdcomplexity indices or values derived from these indices and in responseto indices and values derived from these indices or a variation in theindices and values derived from these indices exceeding a predeterminedthreshold, generates an alert message. Data processor 20 substantiallycontinuously performs the monitoring for at least a 24 hour period.Further, data processor 20 uses predetermined mapping information,associating ranges of the indices or values derived from these indiceswith corresponding medical conditions, in comparing the indices orvalues derived from these indices with the ranges and generates an alertmessage indicating a potential medical condition. The predeterminedmapping information associates ranges of the indices or values derivedfrom these indices with particular patient demographic characteristicsand with corresponding medical conditions and the data processor usespatient demographic data including at least one of, age weight, genderand height in comparing the indices or values derived from these indiceswith the ranges and generating an alert message indicating a potentialmedical condition. The process of FIG. 8 terminates at step 831.

A processor as used herein is a device for executing machine-readableinstructions stored on a computer readable medium, for performing tasksand may comprise any one or combination of, hardware and firmware. Aprocessor may also comprise memory storing machine-readable instructionsexecutable for performing tasks. A processor acts upon information bymanipulating, analyzing, modifying, converting or transmittinginformation for use by an executable procedure or an information device,and/or by routing the information to an output device. A processor mayuse or comprise the capabilities of a controller or microprocessor, forexample, and is conditioned using executable instructions to performspecial purpose functions not performed by a general purpose computer. Aprocessor may be coupled (electrically and/or as comprising executablecomponents) with any other processor enabling interaction and/orcommunication there-between. A user interface processor or generator isa known element comprising electronic circuitry or software or acombination of both for generating display images or portions thereof. Auser interface comprises one or more display images enabling userinteraction with a processor or other device.

An executable application, as used herein, comprises code or machinereadable instructions for conditioning the processor to implementpredetermined functions, such as those of an operating system, a contextdata acquisition system or other information processing system, forexample, in response to user command or input. An executable procedureis a segment of code or machine readable instruction, sub-routine, orother distinct section of code or portion of an executable applicationfor performing one or more particular processes. These processes mayinclude receiving input data and/or parameters, performing operations onreceived input data and/or performing functions in response to receivedinput parameters, and providing resulting output data and/or parameters.A user interface (UI), as used herein, comprises one or more displayimages, generated by a user interface processor and enabling userinteraction with a processor or other device and associated dataacquisition and processing functions.

The UI also includes an executable procedure or executable application.The executable procedure or executable application conditions the userinterface processor to generate signals representing the UI displayimages. These signals are supplied to a display device which displaysthe image for viewing by the user. The executable procedure orexecutable application further receives signals from user input devices,such as a keyboard, mouse, light pen, touch screen or any other meansallowing a user to provide data to a processor. The processor, undercontrol of an executable procedure or executable application,manipulates the UI display images in response to signals received fromthe input devices. In this way, the user interacts with the displayimage using the input devices, enabling user interaction with theprocessor or other device. The functions and process steps herein may beperformed automatically or wholly or partially in response to usercommand. An activity (including a step) performed automatically isperformed in response to executable instruction or device operationwithout user direct initiation of the activity.

The system and processes of FIGS. 1-6 and 8 are not exclusive. Othersystems, processes and menus may be derived in accordance with theprinciples of the invention to accomplish the same objectives. Althoughthis invention has been described with reference to particularembodiments, it is to be understood that the embodiments and variationsshown and described herein are for illustration purposes only.Modifications to the current design may be implemented by those skilledin the art, without departing from the scope of the invention. Thesystem performs blood pressure derived complexity data mapping forinvasive and non-invasive blood pressure waveform data at multipleanatomical sites to identify medical conditions for different kinds ofclinical application events. Further, the processes and applicationsmay, in alternative embodiments, be located on one or more (e.g.,distributed) processing devices on a network linking the units ofFIG. 1. Any of the functions and steps provided in FIGS. 1-6 and 8 maybe implemented in hardware, software or a combination of both.

Appendix Symbolic Dynamics and Complexity Calculation

For simplicity, an embodiment considers 0-1 strings s₁s₂ . . . s_(n)(s_(i) is character 0 or 1, i=1, 2, . . . , n). Let S, Q denote,respectively, two strings, and SQ be the concatenation of S and Q, whilestring SQπ is derived from SQ after its last character is deleted (πmeans the operation to delete the last character). Let v(SQπ) denote thevocabulary of all different substrings of SQπ. At the beginning, c(n)=1,S=s₁, Q=s₂, therefore, SQπ=s₁. For generalization, now suppose S=s₁s₂ .. . s_(r), Q=s_(r+1); if Q∈v(SQπ), then s_(r+1) is a substring of s₁s₂ .. . s_(r), therefore S doesn't change, and renew Q to be s_(r+1)s_(r+2),then judge if Q belongs to v(SQπ) or not; and doing so in this way untilQ∉v(SQπ), now suppose Q=s_(r+1)s_(r+2) . . . s_(r+i), which is not asubstring of s₁s₂ . . . s_(r)s_(r+1) . . . s_(r+i+1), thus increase c(n)by one. Thereafter combine S with Q and S is renewed to be S=s₁s₂ . . .s_(r)s_(r+1) . . . s_(r+i), while take Q as Q=s_(r+i+1). Repeat aboveprocedures until Q is the last character, at this time the number ofdifferent substrings of s₁s₂ . . . s_(n) is c(n), i.e, the measure ofcomplexity. Using two simple operations of comparison and accumulation,the computation of c(n) is implemented. FIG. 7 shows a flowchart of aprocess for complexity calculation of symbolic strings S=s₁s₂ . . . s.

Complexity Information Theory

The information extracted from the coarse-grain symbol dynamic sequencesis limited, and speed information is not obtained by just complexitymeasurements. In abnormal cardiac signal analysis, a clinician desiresto obtain accurate pathological information, such as for body fluid andnerve control interdiction, as well as of abnormal cardiac impedancesignal extraction. The extracted complexity rate information is used toconstruct a correct and reasonable relationship between pathology anddiagnosis parameters. On the basis of established complexity measuresand the complexity method of extracted system features, the systempresent a method for complexity study involving symbolic dynamic systemcomplexity rate information. The underlying cause of non-stationarydynamic change is uncovered with the help of this method.

Given a dynamic system time sequence X={x₁, x₂, . . . , x_(i), . . . },there exists subsequence L_(i), L_(i)={x₁, x₂, . . . , x_(i)}, in whichi=1, 2, . . . , n;

Utilizing the Lempel-Ziv (L-Z) complexity, corresponding complexity iscomputed for each subsequence L_(i); L_(i) and corresponds to complexityc_(i).

C.1. Definition (Finite Sequence Complexity)

Suppose sequence X={x₁, x₂, . . . , x_(i), . . . }, there existssubsequence L_(i), L_(i)={x₁, x₂, . . . , x_(i)}, in which i=1, 2, . . .n; define c_(n)={c₁, c₂, . . . , c_(n)} as the corresponding complexitymeasure sequence of the sequence X_(n), in which c_(i) is the sequencecomplexity of the L_(i), X_(n) is the finite time sequence of X.

C.2. Definition (Time Sequence Complexity Rate)

Given a finite time sequence X={x₁, x₂, . . . , x_(i), . . . }, thecorresponding finite complexity sequence is c={c₁, c₂, . . . c_(n)},define complexity as follows:

${{cc}(n)} = \frac{c_{n_{i}} - c_{n_{j}}}{n_{i} - n_{j}}$

in which n_(i)−n_(j) is at least larger than Takens' embedding dimensionin order to avoid spurious computation. cc(n) reflects the speed of thecomplexity change of the finite time sequence.

According to this definition, the complexity rate of the whole timesequence X(n) can be calculated from slope rate of the sequence fittingpolynomial:

cc[x(n)]=DIFF[x(n)]

Based on the definition above, it is deduced:C.2.1 when the time sequence is an infinite subsequence of a stochasticprocedure, the corresponding maximum complexity is infinite and thecomplexity rate is 1.C.2.2 when the time sequence is a finite subsequence of a stochasticprocedure, the corresponding maximum complexity is a finite value andthe complexity rate is 1.C.2.3 when the time sequence is a subsequence of a periodic procedure,the corresponding complexity of the infinite subsequence is equal tothat of finite effective subsequence and is a finite value. (Here thefinite effective subsequence means that the length of the time sequenceis enough for the effective complexity computation). That is, given aperiodic time sequence X={x₁, x₂, . . . , x_(i), . . . }, there exists aconstant N, when i>N, such that:

cc(i)=c,

in which the constant c is a finite value.(Note that: to achieve algorithm standard and ease of comparison, thecomputing complexity of the time sequence has been standardized.)C.2.4 when the time sequence is the output of a deterministic chaoticsystem, the corresponding complexity of its subsequence increases withthe time series length. And if the corresponding complexity rate iscc(n)_(chaos), then the cc(n)_(chaos) is less than 1. And cc(n)_(chaos)increases with the number of chaotic system dimension.C.2.5 given a discrete time series of an arbitrary continuousdeterministic chaotic system or a random system and the correspondingsymbolic series complexity rate is cc_(m), the maximum complexity can beapproximately computed as follows:

c _(x) =cc _(m)·1(x)

in which the c_(x) is the time sequence complexity and the 1(x) is thelength of the symbol series (here linear fitting is used).

C.3. Average Complexity

Given a limited dynamic time sequence X={x₁, x₂, . . . , x_(n)}, inwhich n<∞; the corresponding complexity sequence is c_(x)={c₁, c₂, . . ., c_(n)},

Then:

$\overset{\_}{c_{x}} = {\lim\limits_{n->\infty}\; {\frac{1}{N}{\sum\limits_{i = 1}^{n}c_{i}}}}$

Suppose the original procedure is continuous, the corresponding averagecomplexity:

$\overset{\_}{c_{x}} = {\frac{1}{T}{\int_{0}^{T}{{cc}_{i}{x}}}}$

1. A system for heart performance characterization and abnormality detection, comprising: an interface for receiving digitized electrical signals representing blood pressure waveforms over one or more heart beat cycles comprising, a first digital data sequence representing normal blood pressure of a patient, a second digital data sequence representing random blood pressure of a normal patient and a third digital data sequence representing a potentially abnormal blood pressure of a patient; and a complexity processor for calculating first, second and third complexity indices for the corresponding first, second and third digital data sequences respectively; and a correlation processor for using the calculated first, second and third complexity indices to calculate one or more measures indicating deviation of said potentially abnormal blood pressure of said patient from a normal value.
 2. A system according to claim 1, wherein said interface substantially synchronizes the first, second and third digital data sequences.
 3. A system according to claim 2, wherein said interface substantially synchronizes the first, second and third digital data sequences in response to a peak or minimum value in the corresponding blood pressure waveforms.
 4. A system according to claim 1, wherein said first, second and third complexity indices comprise S_(i), S_(R) and S_(j) respectively and said correlation processor calculates at least one of (a) C(s_(j))−C(s′_(R)) representing the complexity distance (nonlinearity) from a current blood pressure waveform to a random waveform and (b) C(s_(i))−C(s_(R)) representing a complexity distance from a normal (baseline) pressure waveform to a random waveform.
 5. A system according to claim 4, wherein said correlation processor calculates ratio of (a) and (b).
 6. A system according to claim 1, including a data processor for monitoring said first, second and third complexity indices or values derived from these indices and in response to indices and values derived from these indices or a variation in the indices and values derived from these indices exceeding a predetermined threshold, generating an alert message.
 7. A system according to claim 6, wherein said data processor substantially continuously performs the monitoring for at least a 24 hour period.
 8. A system according to claim 6, wherein said data processor uses predetermined mapping information, associating ranges of said indices or values derived from these indices with corresponding medical conditions, in comparing said indices or values derived from these indices with said ranges and generates an alert message indicating a potential medical condition.
 9. A system according to claim 8, wherein said predetermined mapping information associates ranges of said indices or values derived from these indices with particular patient demographic characteristics and with corresponding medical conditions and said data processor uses patient demographic data including at least one of age weight, gender and height in comparing said indices or values derived from these indices with said ranges and generating an alert message indicating a potential medical condition.
 10. A system according to claim 8, wherein said first digital data sequence representing normal blood pressure of a patient is provided from stored blood pressure data of at least one of (a) said patient and (b) a patient having similar demographic characteristics as said patient including at least one of, age weight, gender and height and similar medical conditions as said patient.
 11. A method for heart performance characterization and abnormality detection, comprising the activities of: receiving digitized electrical signals representing blood pressure waveforms over one or more heart beat cycles comprising, a first digital data sequence representing normal heart blood pressure of a patient, a second digital data sequence representing random heart blood pressure of a normal patient and a third digital data sequence representing a potentially abnormal heart blood pressure of a patient; and calculating first, second and third complexity indices for the corresponding first, second and third digital data sequences respectively; and using the calculated first, second and third complexity indices to calculate one or more measures indicating deviation of said potentially abnormal heart blood pressure of said patient from a normal range.
 12. A system for heart performance characterization and abnormality detection, comprising: an interface for receiving a digitized electrical signal representing a blood pressure waveform over one or more heart beat cycles; a data processor for performing a Fourier transform on said digitized electrical signal to derive data representing individual frequency components of said digitized electrical signal; a calculation processor for determining measures comprising, (a) a first value representing a summation of the square of individual Fourier transform values of corresponding individual components of said individual frequency components and (b) a second value representing a square of the summation of individual Fourier transform values of corresponding individual components of said individual frequency components; and a correlation processor for using the calculated first and second values to calculate one or more measures indicating deviation of said potentially abnormal blood pressure of said patient from a normal value.
 13. A system according to claim 12, wherein said interface synchronizes said digitized electrical signal.
 14. A system according to claim 13, wherein said interface substantially synchronizes said digitized electrical signal in response to a peak or minimum value in the corresponding blood pressure waveforms.
 15. A system according to claim 12, wherein said correlation processor calculates a ratio employing said first and second values in deriving a measure indicating deviation of said potentially abnormal blood pressure of said patient from a normal value.
 16. A system according to claim 15, wherein said correlation processor calculates a ratio employing said first and second values comprising, ${{\gamma_{xx}(f)} = \frac{\frac{1}{N}{{\sum\limits_{n = 1}^{N}{X_{n}(f)}}}^{2}}{\frac{1}{N}{\sum\limits_{n = 1}^{N}{{X_{n}(f)}}^{2}}}},$ where data points are, x₁, . . . x_(N) and corresponding Fourier transform is X₁, . . . X_(N).
 17. A system according to claim 15, wherein said correlation processor calculates a ratio employing said first and second values comprising, ${spectrum\_ coherence} = \frac{\overset{\_}{\gamma}}{\overset{\_}{\gamma} + {\sum\limits_{\Omega}{\Theta \left( {{{\hat{\gamma}\left( ^{j\; w} \right)} - \overset{\_}{y}}} \right)}}}$ with ${\Theta (x)} = {\begin{Bmatrix} {0.0,} & {if} & {x < {\Theta \overset{\_}{\gamma}}} \\ {x,} & {if} & {x \geq {\Theta \overset{\_}{\gamma}}} \end{Bmatrix}.}$ where 0<Θ<1 and the Spectrum coherence index is near 1 when the ongoing blood pressure waveform signal is healthy and spectrum coherence index of the blood pressure waveform decreases towards zero due to abnormality in a blood pressure waveform. 